🚀 Retail Price Optimization Dashboard

A comprehensive, production-ready retail price optimization dashboard with machine learning models, price prediction, competitor analysis, and dynamic pricing strategies.

✨ Features

📊 Dashboard Capabilities

• Interactive Visualizations: Multiple chart types including histograms, box plots, scatter plots, correlation heatmaps
• Real-time Price Prediction Calculator: Input product parameters for instant predictions
• Advanced Analytics Dashboard: KPI metrics, market analysis, and data exploration tools
• Export & Reporting: Download filtered data in CSV, Excel, or JSON format

🤖 Machine Learning Models

• Decision Tree Regressor
• Random Forest Regressor
• Gradient Boosting Regressor
• Linear/Ridge/Lasso Regression
• Model evaluation with multiple metrics (R², RMSE, MAE, MAPE)
• Cross-validation support
• Feature importance analysis

💰 Price Optimization

• Multiple Pricing Strategies:
  • Cost-plus pricing
  • Competitor-based pricing
  • Value-based pricing
  • Dynamic pricing
  • Penetration pricing
  • Price skimming
• Price elasticity analysis
• Competitive position analysis
• Profit margin optimization

📈 Analytics Engine

• Statistical analysis (descriptive stats, correlations, hypothesis testing)
• Trend analysis and forecasting
• Category performance metrics
• Customer behavior analysis
• Product segmentation

🔌 API Endpoints

• RESTful API with FastAPI
• Price prediction endpoint
• Optimization endpoints
• Batch processing support
• Health monitoring

🏗️ Project Structure

Retail-Prices/
├── app.py                    # Main Streamlit application
├── Optimized.py              # Enhanced Streamlit dashboard
├── config.py                 # Configuration settings
├── data_validation.py        # Data validation utilities
├── retail_price.csv          # Dataset
├── requirements.txt          # Python dependencies
├── Dockerfile                # Docker configuration
├── docker-compose.yml        # Docker Compose setup
│
├── models/                   # Machine Learning Models
│   ├── __init__.py
│   ├── price_predictor.py    # Price prediction models
│   ├── model_evaluator.py    # Model evaluation tools
│   └── feature_importance.py # Feature importance analysis
│
├── utils/                    # Utility Modules
│   ├── __init__.py
│   ├── price_optimizer.py    # Price optimization engine
│   ├── data_preprocessing.py # Data preprocessing utilities
│   ├── analytics.py          # Analytics engine
│   ├── visualizations.py     # Chart building utilities
│   ├── logger.py             # Logging configuration
│   ├── database.py           # Database connector
│   └── report_generator.py   # Report generation
│
├── api/                      # API Module
│   └── main.py               # FastAPI endpoints
│
├── pages/                    # Streamlit Pages
│   ├── 1_📦_Inventory_Analysis.py
│   ├── 2_📈_Sales_Forecast.py
│   └── 3_🏪_Competitor_Analysis.py
│
├── tests/                    # Test Suite
│   ├── conftest.py           # Pytest configuration
│   └── test_modules.py       # Unit tests
│
└── .github/                  # GitHub Actions
    └── workflows/
        └── ci.yml            # CI/CD pipeline

🚀 Quick Start

Prerequisites

• Python 3.9 or higher
• pip package manager

Installation

1. Clone the repository

git clone https://github.com/kamrankamrankhan/Retail-Prices.git
cd Retail-Prices

2. Create virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Run the Streamlit app

streamlit run app.py

5. Run the FastAPI server (optional)

uvicorn api.main:app --reload --port 8000

Docker Deployment

# Build and run with Docker Compose
docker-compose up -d

# Access the dashboard
# Streamlit: http://localhost:8501
# API: http://localhost:8000

📊 Usage

Price Prediction

from models.price_predictor import PricePredictor
import pandas as pd

# Load data
data = pd.read_csv('retail_price.csv')
data['comp_price_diff'] = data['unit_price'] - data['comp_1']

# Initialize and train model
predictor = PricePredictor(model_type='random_forest')
predictor.fit(data)

# Make prediction
predicted_price = predictor.predict_single(
    qty=100,
    unit_price=50.0,
    comp_price=45.0,
    product_score=4.5
)
print(f"Predicted Price: ${predicted_price:.2f}")

Price Optimization

from utils.price_optimizer import PriceOptimizer, PricingStrategy
import pandas as pd

data = pd.read_csv('retail_price.csv')
optimizer = PriceOptimizer(data)

# Optimize price using competitor-based strategy
result = optimizer.optimize_price(
    product_id='bed1',
    strategy=PricingStrategy.COMPETITOR_BASED,
    position='competitive'
)

print(f"Optimal Price: ${result.optimal_price}")
print(f"Expected Revenue: ${result.expected_revenue}")

Data Preprocessing

from utils.data_preprocessing import DataPreprocessor

preprocessor = DataPreprocessor(data)
preprocessor.remove_duplicates()\
    .handle_missing_values(strategy='mean')\
    .remove_outliers(method='iqr')\
    .create_features()

processed_data = preprocessor.get_processed_data()

🔌 API Endpoints

Endpoint	Method	Description
/	GET	API info
/health	GET	Health check
/predict	POST	Price prediction
/optimize	POST	Price optimization
/optimize/batch	POST	Batch optimization
/products	GET	List products
/categories	GET	List categories
/analytics/summary	GET	Analytics summary

Example API Request

# Predict price
curl -X POST "http://localhost:8000/predict" \
  -H "Content-Type: application/json" \
  -d '{
    "qty": 100,
    "unit_price": 50.0,
    "comp_1": 45.0,
    "product_score": 4.5
  }'

🧪 Testing

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ -v --cov=. --cov-report=html

# Run specific test file
python tests/test_modules.py

📈 Dashboard Pages

1. Main Dashboard (app.py) - Core analytics and visualizations
2. Inventory Analysis - Stock levels and product performance
3. Sales Forecast - Predictive sales analytics
4. Competitor Analysis - Market position comparison

🛠️ Configuration

Edit config.py to customize:

# Application settings
APP_TITLE = "Retail Price Analytics Dashboard"
DATA_FILE = "retail_price.csv"

# Model settings
DEFAULT_TEST_SIZE = 20
DEFAULT_RANDOM_STATE = 42

# Data validation thresholds
MAX_PRICE_THRESHOLD = 10000
MIN_QUALITY_SCORE = 70

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Streamlit for the amazing dashboard framework
• Scikit-learn for machine learning utilities
• Plotly for interactive visualizations
• FastAPI for the high-performance API framework

📞 Support

For issues and feature requests, please use the GitHub Issues page.

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